Insecure Deserialization in Adonisjs with Jwt Tokens

In AdonisJS, JWTs are typically created and verified using the jwt provider from @adonisjs/auth. When a payload is encoded into a token, it is serialized into a JSON string and then signed. If an application deserializes untrusted data — for example, by base64‑decoding and parsing parts of the token outside the verification step — and then reconstructs objects from that data, insecure deserialization can occur. This is especially risky when the application uses custom claim transformations or manually decodes the token payload without strict validation.

A concrete exposure pattern arises when a developer extracts a claim from a JWT using jwt.verify but then passes the decoded object into functions that invoke methods based on string keys (e.g., polymorphic handling of user roles or dynamic configuration). If an attacker can influence the token payload before signing — for instance, through a compromised client or a misconfigured issuer — they may inject serialized objects that, when deserialized on the server, lead to unexpected behavior, prototype pollution, or remote code execution via gadget chains in Node.js. Even with signature verification, trusting the content of the payload without schema validation can lead to Insecure Deserialization when the application logic interprets nested objects or arrays in unsafe ways.

Consider a route that accepts a JWT and uses a claim to determine access to a resource loader. If the deserialized payload is used to dynamically require modules or to hydrate models without strict type checks, an attacker could supply a token with a payload like { "role": "Admin", "transform": { "__proto__": { "isAdmin": true } } }. During deserialization, the application might inadvertently merge this into global objects or configuration, altering behavior in memory. Although JWT signatures prevent tampering after issuance, the vulnerability exists when the application treats the decoded payload as inherently safe and performs deep deserialization without schema enforcement.

In the context of OWASP API Top 10, this maps to #1: Broken Object Level Authorization when deserialized objects are used to make authorization decisions, and it can intersect with Injection and Security Misconfiguration if the deserialization logic is tied to external data. middleBrick’s LLM/AI Security checks can detect exposed debug endpoints or misconfigured public tokens that facilitate such attacks, while its BOLA/IDOR and Property Authorization tests validate whether object-level access controls are enforced after deserialization.

Real-world testing with active probes — such as attempting to override instructions or exfiltrate data via manipulated claims — helps uncover whether the API’s JWT handling is vulnerable to insecure deserialization patterns. Always validate the structure of JWT claims, avoid dynamic evaluation of deserialized content, and rely on strict schemas to ensure that only expected data types and properties are accepted.

To mitigate Insecure Deserialization when working with JWTs in AdonisJS, enforce strict schema validation on every decoded payload and avoid any dynamic behavior based on raw token content. Use the built-in JWT utilities to verify signatures and parse claims, and apply typed validation libraries such as celebrate or @ioc:AdonisJS/Validator to ensure the structure and types of claims match expectations.

Example of secure token generation and verification in AdonisJS:

import { BaseProvider } from '@ioc:Adonis/Core/Provider'
import { JwtProviderContract } from '@ioc:Adonis/Addons/Jwt'
import { schema, rules } from '@ioc:Adonis/Core/Validator'

export default class AppProvider extends BaseProvider {
  public register() {
    this.app.singleton('Adonis/Addons/Jwt', () => {
      const { Jwt } = require('@adonisjs/auth')
      return new Jwt(this.app, {
        enabled: true,
        provider: 'jwt',
        secret: process.env.JWT_SECRET,
        token: {
          expiresIn: '2h',
        },
      })
    })
  }
}

Example of safe verification and claim validation:

import { HttpContextContract } from '@ioc:Adonis/Core/HttpContext'
import { schema, rules } from '@ioc:Adonis/Core/Validator'

const tokenSchema = schema.create({
  payload: schema.object({
    sub: schema.string.optional(),
    role: schema.enum(['user', 'admin'] as const),
    permissions: schema.array.optional(schema.string()),
    iat: schema.number.positive(),
    exp: schema.number.positive(),
  }),
})

export async function validateToken({ request, response }: HttpContextContract) {
  const token = request.headers().authorization?.replace('Bearer ', '')
  if (!token) {
    return response.unauthorized('Missing token')
  }

  const jwt = use('Adonis/Addons/Jwt') as any
  let verified
  try {
    verified = jwt.verify(token)
  } catch {
    return response.badRequest('Invalid token')
  }

  // Validate structure and types
  const payload = verified.payload
  const compiled = tokenSchema.compile()
  const { error, value } = compiled(payload)
  if (error) {
    return response.badRequest('Invalid token claims')
  }

  // Use value.role and value.permissions for authorization
  return { user: value }
}

Apply the same validation before any deserialization or role-based access logic. Avoid using JSON.parse on token claims or passing raw payloads into eval-like mechanisms. In the dashboard, track score and per-category breakdowns to monitor JWT-related findings, and configure the Pro plan for continuous monitoring and alerting when risk scores degrade.

Finally, leverage the GitHub Action to fail builds if risk scores drop below your threshold, and use the MCP Server to scan APIs directly from your AI coding assistant during development. These integrations help catch insecure deserialization patterns early, before tokens reach production.